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Related Experiment Video

Updated: Jul 1, 2025

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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Kernel Bayesian nonlinear matrix factorization based on variational inference for human-virus protein-protein

Yingjun Ma1, Yongbiao Zhao2, Yuanyuan Ma3,4

  • 1School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China.

Scientific Reports
|March 7, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces VKBNMF, a novel computational model for predicting human-virus protein-protein interactions (PPIs). VKBNMF enhances antiviral drug discovery by improving the accuracy and efficiency of identifying these crucial interactions.

Keywords:
Automatic rank determinationBayesian matrix factorizationHuman proteinsVariational inferenceViral proteins

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Area of Science:

  • Computational biology
  • Virology
  • Drug discovery

Background:

  • Human-virus protein-protein interactions (PPIs) are crucial for understanding viral infection mechanisms and developing antiviral therapies.
  • Existing computational models for PPI prediction often suffer from manual hyperparameter tuning, limiting efficiency and generalizability.

Purpose of the Study:

  • To develop an efficient and accurate computational model for predicting human-virus PPIs.
  • To address the limitations of existing models by incorporating automatic parameter search and rank determination.

Main Methods:

  • Proposed a kernel Bayesian logistic matrix decomposition model with automatic rank determination (VKBNMF).
  • Integrated auxiliary information and Bayesian framework with prior probabilities for latent variables.
  • Implemented a variational inference framework for efficient computation.

Main Results:

  • VKBNMF achieved high average AUPR values (0.9101-0.9517) on benchmark datasets for paired PPI prediction.
  • Demonstrated a higher hit rate in predicting interactions involving new human or viral proteins.
  • Case studies confirmed VKBNMF's effectiveness as a prediction tool.

Conclusions:

  • VKBNMF offers an efficient and accurate approach for predicting human-virus PPIs.
  • The model's automatic parameter search and rank determination enhance its computational efficiency and generalization ability.
  • VKBNMF shows promise for advancing antiviral drug development through improved PPI prediction.